This objective can be optimized with RL algorithms by setting rewards to \(r_t = \log p_{E}(s_{t+1}|s_t)\)  and maximizing the undiscounted return, while penalizing the negative entropy of the policy. This suggests a practical algorithm where we can use a finite horizon variant of Soft Actor-Critic [1]}, which maximizes a reward signal alongside the entropy of the policy.
